Dynamic graphical methods have two important properties: direct manipulation of graphical elements on a computer graphics screen and virtually instantaneous change of the elements. The data analyst takes an action through manual manipulation of an input device and something happens in real time on the screen. These computing capabilities provide a new medium for the invention of graphical methods for data analysis. A collection of such methods--identification, deletion, linking, brushing, scaling, rotation, and dynamic parameter control--is reviewed. Those who develop dynamic methods must deal with a number of computer hardware and software issues, because for a dynamic method to work there must be a sufficiently fast flow of information along the channel that starts with the analyst's input and ends with the changed graph. Furthermore, for a dynamic method to be useful, the visual and manual tasks must be easy to perform. Several computing issues dealing with speed and ease of use--system bandwidth, input and output devices, low-level algorithms, device independent graphics, and data analysis environments--are reviewed.

The problem of predicting a future measurement on an individual given the past measurements is discussed under nonparametric and parametric growth models. The efficiencies of different methods of prediction are assessed by cross-validation or leave-one-out technique in each of three data sets and the results are compared. Under nonparametric models, direct and inverse regression methods of prediction are described and their relative advantages and disadvantages are discussed. Under parametric models polynomial and factor analytic type growth curves are considered. Bayesian and empirical Bayesian methods are used to deal with unknown parameters. A general finding is that much of the information for forecasting is contained in the immediate past few observations or a few summary statistics based on past data. A number of data reduction methods are suggested and analyses based on them are described. The usefulness of the leave-one-out technique in model selection is demonstrated. A new method of calibration is introduced to improve prediction.

This survey is a review of Soviet studies in reliability theory. The stress is on the theoretical work developed in the Soviet Union during the last two decades. Some related work, although not by Soviet scholars, is also described.